DOI: 10.5281/zenodo.19226407 23stabilfr·wdophcgmx| Badge | Metric | Value | Status | Description |
|---|
| [s] | Reviewed Sources | 0% | ○ | ≥80% from editorially reviewed sources |
| [t] | Trusted | 0% | ○ | ≥80% from verified, high-quality sources |
| [a] | DOI | 50% | ○ | ≥80% have a Digital Object Identifier |
| [b] | CrossRef | 0% | ○ | ≥80% indexed in CrossRef |
| [i] | Indexed | 0% | ○ | ≥80% have metadata indexed |
| [l] | Academic | 0% | ○ | ≥80% from journals/conferences/preprints |
| [f] | Free Access | 50% | ○ | ≥80% are freely accessible |
| [r] | References | 2 refs | ○ | Minimum 10 references required |
| [w] | Words [REQ] | 3,481 | ✓ | Minimum 2,000 words for a full research article. Current: 3,481 |
| [d] | DOI [REQ] | ✓ | ✓ | Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226407 |
| [o] | ORCID [REQ] | ✗ | ✗ | Author ORCID verified for academic identity |
| [p] | Peer Reviewed [REQ] | — | ✗ | Peer reviewed by an assigned reviewer |
| [h] | Freshness [REQ] | 0% | ✗ | ≥80% of references from 2025–2026. Current: 0% |
| [c] | Data Charts | 0 | ○ | Original data charts from reproducible analysis (min 2). Current: 0 |
| [g] | Code | — | ○ | Source code available on GitHub |
| [m] | Diagrams | 2 | ✓ | Mermaid architecture/flow diagrams. Current: 2 |
| [x] | Cited by | 0 | ○ | Referenced by 0 other hub article(s) |
Score = Ref Trust (14 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Medical artificial intelligence has long suffered from a critical epistemic gap: models produce predictions without producing justifications. Clinicians, regulators, and patients cannot evaluate the validity of a decision if they can only see its output. ScanLab addresses this gap through a deliberate architectural choice — making explainability a mandatory, non-negotiable layer of the inferenc...
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